This study investigates the inventory re-slotting process within warehouse operations, employing real-world data from the retail industry. The primary objectives are to minimize total operational time and reduce the pickers’ travel distance during item retrievals. Total operational time consists of item re-arrangement time, which is required when previously picked items are heavier, loading and unloading durations, and the pickers’ walking time, computed using Manhattan distance. In slotting decisions, two important factors are considered, including the individual weight of items and their respective consumption rates. Between the two factors, item weight receives higher priority than consumption rate. Item weights are classified into three distinct weight categories: light, medium, and heavy, whereas consumption rate usually fluctuates over time and necessitates periodic re-slotting. For a given pick list, the Traveling Salesman Problem (TSP) model is employed to determine the optimal picking route within each prioritized weight category. The effectiveness of the proposed approach is evaluated based on improvements in picker productivity. Preliminary results indicate that optimized inventory re-slotting can reduce labor costs, thereby significantly enhancing overall warehouse efficiency.